COVID-19 Variants Analysis
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Time Series Analysis
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Max lag exceeds data available. Using max lag: 21Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE[1] "sf" "data.frame" [1] "Afghanistan" "Africa" "Albania"
[4] "Algeria" "Andorra" "Angola"
[7] "Anguilla" "Antigua and Barbuda" "Argentina"
[10] "Armenia" "Aruba" "Asia"
[13] "Australia" "Austria" "Azerbaijan"
[16] "Bahamas" "Bahrain" "Bangladesh"
[19] "Barbados" "Belarus" "Belgium"
[22] "Belize" "Benin" "Bermuda"
[25] "Bhutan" "Bolivia" "Bonaire Sint Eustatius and Saba"
[28] "Bosnia and Herzegovina" "Botswana" "Brazil"
[31] "British Virgin Islands" "Brunei" "Bulgaria"
[34] "Burkina Faso" "Burundi" "Cambodia"
[37] "Cameroon" "Canada" "Cape Verde"
[40] "Cayman Islands" "Central African Republic" "Chad"
[43] "Chile" "China" "Colombia"
[46] "Comoros" "Congo" "Cook Islands"
[49] "Costa Rica" "Cote d'Ivoire" "Croatia"
[52] "Cuba" "Curacao" "Cyprus"
[55] "Czechia" "Democratic Republic of Congo" "Denmark"
[58] "Djibouti" "Dominica" "Dominican Republic"
[61] "Ecuador" "Egypt" "El Salvador"
[64] "Equatorial Guinea" "Eritrea" "Estonia"
[67] "Eswatini" "Ethiopia" "Europe"
[70] "European Union" "Faeroe Islands" "Falkland Islands"
[73] "Fiji" "Finland" "France"
[76] "French Polynesia" "Gabon" "Gambia"
[79] "Georgia" "Germany" "Ghana"
[82] "Gibraltar" "Greece" "Greenland"
[85] "Grenada" "Guam" "Guatemala"
[88] "Guernsey" "Guinea" "Guinea-Bissau"
[91] "Guyana" "Haiti" "High income"
[94] "Honduras" "Hong Kong" "Hungary"
[97] "Iceland" "India" "Indonesia"
[100] "International" "Iran" "Iraq"
[103] "Ireland" "Isle of Man" "Israel"
[106] "Italy" "Jamaica" "Japan"
[109] "Jersey" "Jordan" "Kazakhstan"
[112] "Kenya" "Kiribati" "Kosovo"
[115] "Kuwait" "Kyrgyzstan" "Laos"
[118] "Latvia" "Lebanon" "Lesotho"
[121] "Liberia" "Libya" "Liechtenstein"
[124] "Lithuania" "Low income" "Lower middle income"
[127] "Luxembourg" "Macao" "Madagascar"
[130] "Malawi" "Malaysia" "Maldives"
[133] "Mali" "Malta" "Marshall Islands"
[136] "Mauritania" "Mauritius" "Mexico"
[139] "Micronesia (country)" "Moldova" "Monaco"
[142] "Mongolia" "Montenegro" "Montserrat"
[145] "Morocco" "Mozambique" "Myanmar"
[148] "Namibia" "Nauru" "Nepal"
[151] "Netherlands" "New Caledonia" "New Zealand"
[154] "Nicaragua" "Niger" "Nigeria"
[157] "Niue" "North America" "North Macedonia"
[160] "Northern Cyprus" "Northern Mariana Islands" "Norway"
[163] "Oceania" "Oman" "Pakistan"
[166] "Palau" "Palestine" "Panama"
[169] "Papua New Guinea" "Paraguay" "Peru"
[172] "Philippines" "Pitcairn" "Poland"
[175] "Portugal" "Puerto Rico" "Qatar"
[178] "Romania" "Russia" "Rwanda"
[181] "Saint Helena" "Saint Kitts and Nevis" "Saint Lucia"
[184] "Saint Pierre and Miquelon" "Saint Vincent and the Grenadines" "Samoa"
[187] "San Marino" "Sao Tome and Principe" "Saudi Arabia"
[190] "Senegal" "Serbia" "Seychelles"
[193] "Sierra Leone" "Singapore" "Sint Maarten (Dutch part)"
[196] "Slovakia" "Slovenia" "Solomon Islands"
[199] "Somalia" "South Africa" "South America"
[202] "South Korea" "South Sudan" "Spain"
[205] "Sri Lanka" "Sudan" "Suriname"
[208] "Sweden" "Switzerland" "Syria"
[211] "Taiwan" "Tajikistan" "Tanzania"
[214] "Thailand" "Timor" "Togo"
[217] "Tokelau" "Tonga" "Trinidad and Tobago"
[220] "Tunisia" "Turkey" "Turkmenistan"
[223] "Turks and Caicos Islands" "Tuvalu" "Uganda"
[226] "Ukraine" "United Arab Emirates" "United Kingdom"
[229] "United States" "United States Virgin Islands" "Upper middle income"
[232] "Uruguay" "Uzbekistan" "Vanuatu"
[235] "Vatican" "Venezuela" "Vietnam"
[238] "Wallis and Futuna" "World" "Yemen"
[241] "Zambia" "Zimbabwe" `summarise()` has grouped output by 'Country'. You can override using the `.groups` argument.Error: cannot allocate vector of size 421.3 Mb
---
title: "COVID-19 Variants Analysis"
output: html_notebook
---

```{r, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
```

```{r, echo = FALSE, message=FALSE, warning=FALSE, comment=FALSE}
library(tidyverse)
library(scales)
library(plotly)
library(DT)
library(data.table)
library(gtable)
library(plotly)
library(lubridate)
library(vtable)
library(rjson)
library(timetk)
library(Rssa)
```

```{r, echo = FALSE, warning=FALSE}
covid <- read_csv("data/owid-covid-data.csv",show_col_types = FALSE)
#variants <- read_csv("data/covid-variants.csv",show_col_types = FALSE)

gisaid <- as.data.frame(fread("data/gisaid_variants_statistics.tsv")) %>% 
  rename(date = `Week prior to`,
         count = `Submission Count`,
         perc_sequences = `% per Country and Week`,
         total = `Total per Country and Week`,
         variant = Value) %>% 
  mutate(date = ymd(date),
         perc_sequences = round(count / total * 100, 3))# %>% 
#  separate(variant, into = c("variant", "origin"), sep = c("first detected in "))

gisaid_variants <- gisaid %>% 
  filter(Type == "Variant") %>%
  separate(variant, c("variant", "origin"), sep = "first detected in ") %>% 
  select(-Type)
```

```{r}
covid_NAs <- covid %>% 
  group_by(location) %>% 
  summarise_all(funs(sum(is.na(.)))) %>% 
  pivot_longer(cols = -location, names_to = "Variable", values_to = "NAs") %>% 
  mutate(Percent = round(NAs / nrow(covid) * 100 ,2)) %>% 
  arrange(-NAs)
```

```{r}
covid_NAs %>% 
  group_by(location) %>% 
  summarise(total_pct_na = sum(Percent)) %>% 
  arrange(total_pct_na) %>% 
  datatable(filter = 'top')
```

```{r}
#Helper function for filtering data
my_data <- function(country_covid_filter, country_gisaid_filter){
  data <- covid %>% 
    filter(location == country_covid_filter)
  gisaid_data <- gisaid_variants %>% 
    filter(Country == country_gisaid_filter)
  data <- left_join(data, gisaid_data, by = "date")
  data
}

us <- my_data("United States", "USA")
```


```{r "US Plots"}
variants_plot <- ggplot(data = us, aes(x = date)) +
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), position = "dodge", show.legend = FALSE) +
  theme_minimal()


cases_plot <-
  ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_per_million), show.legend = FALSE) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


deaths_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_vaccinations_smoothed_per_million)) +
  theme_minimal()


variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million")  +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


variants_deaths_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant),show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_deaths_smoothed_per_million*5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~./5, name = "New Deaths Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Deaths Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


cases_vaccinations_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_smoothed_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_vaccinated_per_hundred*50)) + 
  scale_y_continuous("New Cases Per Million", sec.axis=sec_axis(~./50, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Cases Per Million vs. People Vaccinated Per Hundred")

deaths_vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_fully_vaccinated_per_hundred/7)) + 
  scale_y_continuous("New Deaths Per Million", sec.axis=sec_axis(~.*7, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Deaths Per Million vs. People Fully Vaccinated Per Hundred")


variants_hospitalizations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = weekly_hosp_admissions_per_million / 5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*5, name = "Hospitalizations Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs Hospitalizations Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = people_fully_vaccinated_per_hundred)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~., name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs People Fully Vaccinated") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot
```

```{r}
vaccinations_plot
```

```{r}
variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_cases_plot
```

```{r}
variants_hospitalizations_plot
```

```{r}
variants_deaths_plot
```


# Time Series Analysis

```{r}
us %>% 
  plot_time_series(date, new_cases_smoothed_per_million)
```

```{r}
us %>% 
  plot_acf_diagnostics(date, new_cases_smoothed_per_million, .show_white_noise_bars = T) 
```


```{r}
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  plot_time_series(date, perc_sequences, .facet_vars=variant, .legend_show = FALSE)
```

```{r}
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  group_by(variant) %>% 
  plot_acf_diagnostics(date, perc_sequences, .show_white_noise_bars = T) 
```

```{r}
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
```

```{r}
unique(gisaid$Country)
```

```{r}
unique(covid$location)
```


```{r}
world_variants <- gisaid %>% 
  group_by(Country) %>% 
  mutate(Country = case_when(
  Country == "USA" ~ "United States",
  TRUE ~ as.character(Country)
  )) %>% 
  summarise(most_recent_date = date[n()], 
            prevalent_variant = variant[date == date[n()] & Type == "Variant" & perc_sequences == max(perc_sequences)]) %>% 
            #prevalent_lineage = variant[date == date[n()] & Type == "Lineage" & perc_sequences == max(perc_sequences)]) %>% 
  arrange(desc(most_recent_date)) %>% 
  rename(location = Country)

world_variants <- left_join(world_variants, covid[, c("location", "iso_code")], by = "location", all.x = TRUE) %>% 
  rename(gu_a3 = iso_code)

world_variants_map <- left_join(world, world_variants, by = "gu_a3", all.x = TRUE)
```

```{r}
p <- ggplot(data = world_variants_map), aes(fill = prevalent_variant)) + 
  geom_sf(show.legend = FALSE) + 
  xlab("Longitude") + 
  ylab("Latitude") + 
  theme(panel.grid.major = element_line(color = gray(.5), linetype = "dashed", size = 0.5), panel.background = element_rect(fill = "aliceblue")) 
```

```{r}
p
```


